{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7c9e2048",
   "metadata": {},
   "source": [
    "# Fine-Tuning ChatGPT for Different Data Sets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e8ddc61",
   "metadata": {},
   "source": [
    "# Imports\n",
    "Here we import all the necessary packages and modules for our fine-tuning process.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b410a579",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import glob\n",
    "import pprint\n",
    "import time\n",
    "import argparse\n",
    "from ast import literal_eval\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "from tqdm import tqdm\n",
    "\n",
    "import wandb\n",
    "from openai import OpenAI\n",
    "import pandas as pd\n",
    "\n",
    "from utils_2 import (\n",
    "    dataset_has_format_errors,\n",
    "    check_token_statistics_and_cost_estimate,\n",
    "    load_dataset_task_prompt_mappings,\n",
    "    write_jsonl,\n",
    ")\n",
    "from utils_1 import task_num_to_task_name, dataset_num_to_dataset_name, plot_count_and_normalized_confusion_matrix, task_to_display_labels\n",
    "\n",
    "# Get the notebook's full path\n",
    "notebook_path = os.getcwd()\n",
    "\n",
    "# Set module_dir to the notebook's directory\n",
    "module_dir = notebook_path\n",
    "\n",
    "#api_key_project = \"\" #MK's Key\n",
    "api_key_project = \"\" #new PRODIGI key\n",
    "\n",
    "client = OpenAI(\n",
    "    api_key=api_key_project,  # This is the default and can be omitted\n",
    ")\n",
    "\n",
    "\n",
    "WANDB_PROJECT_NAME = \"chatgpt_annotations_llm_comparison\"\n",
    "MODEL_NAME = 'gpt-4o-mini-2024-07-18'\n",
    "COMPLETION_RETRIES = 50\n",
    "\n",
    "print(notebook_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "772bcc76",
   "metadata": {},
   "source": [
    "# Utility Functions\n",
    "These functions provide various utilities for tasks such as data loading, token statistics, and other preprocessing steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa7e1cb4",
   "metadata": {},
   "source": [
    "## Configuration Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c79142f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration Variables\n",
    "# Manually set the variables that were previously handled by argparse\n",
    "\n",
    "# Type of task to run inference on\n",
    "task = 1  # Choices: [1]\n",
    "\n",
    "# Dataset to run inference on\n",
    "dataset = 5  # Choices: [1, 2, 3, 4, 5, 6]\n",
    "\n",
    "# Expert Dataset for Second Evaluation\n",
    "dataset_eval = 'x' # Choises: ['x']\n",
    "\n",
    "# Size of the sample to generate\n",
    "sample_size = '100'  # Choices: ['100','200']\n",
    "\n",
    "# Path to the directory to store the generated samples\n",
    "output_dir = '../../annotations/chatGPT/output_fine_tuning/'\n",
    "\n",
    "# Random seed to use\n",
    "seed = 2019\n",
    "\n",
    "# Path to the directory containing the datasets\n",
    "data_dir = '../../annotations/chatGPT/input_fine_tuning/'\n",
    "\n",
    "# Whether to use the full label\n",
    "not_use_full_labels = False\n",
    "\n",
    "# Path to the dataset-task mappings file\n",
    "dataset_task_mappings_fp = os.path.normpath(os.path.join(module_dir, '../../task_mappings', 'dataset_task_mappings.csv'))\n",
    "\n",
    "# Whether to rewrite the dataframe in OpenAI format\n",
    "rewrite_df_in_openai = True\n",
    "\n",
    "# Number of epochs to train the model\n",
    "n_epochs = 5\n",
    "\n",
    "# Batch Size for learning\n",
    "n_batch = 20\n",
    "\n",
    "# Name of the run\n",
    "run_name = 'finetune_chetGPT_hatespeech_prolific'\n",
    "\n",
    "# Temperature to use when generating text\n",
    "temp = 0.0\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71e81ba8",
   "metadata": {},
   "source": [
    "## Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ef6fa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "def map_label_to_completion(label: str, task_num: int, full_label: bool = True) -> str:\n",
    "    new_label = ''\n",
    "\n",
    "    if task_num == 1:\n",
    "        if full_label:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'HATE SPEECH'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'TOXIC SPEECH'\n",
    "            else:\n",
    "                new_label = 'KEINE HATE SPEECH'\n",
    "            assert new_label in ['HATE SPEECH', 'TOXIC SPEECH', 'KEINE HATE SPEECH']\n",
    "        else:\n",
    "            if str(label) in ['1.0', '1']:\n",
    "                new_label = 'A'\n",
    "            elif str(label) in ['2.0', '2']:\n",
    "                new_label = 'C'\n",
    "            else:\n",
    "                new_label = 'B'\n",
    "            assert new_label in ['A', 'B', 'C']\n",
    "\n",
    "    return new_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "02978fd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_train_and_eval_sets(data_dir: str, dataset_num: int, task_num: int, sample_size: int, dataset_eval:str) \\\n",
    "        -> dict[str, pd.DataFrame]:\n",
    "    datasets = dict()\n",
    "\n",
    "    train_dataset_task_files = glob.glob(os.path.join(data_dir, f'ds_{dataset_num}__task_{task_num}_train_set*.csv'))\n",
    "    eval_set_name = f'ds_{dataset_num}__task_{task_num}_eval_set'\n",
    "    datasets[eval_set_name] = pd.read_csv(os.path.join(data_dir, eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    # Load the additional evaluation dataset specified by dataset_eval\n",
    "    second_eval_set_name = f'ds_{dataset_eval}__task_{task_num}_full_eval'\n",
    "    datasets[second_eval_set_name] = pd.read_csv(os.path.join(data_dir, second_eval_set_name + '.csv'), encoding='utf-8')\n",
    "\n",
    "    if sample_size == 'all':\n",
    "        train_dfs_ = {fn.strip('.csv'): pd.read_csv(fn, encoding='utf-8') for fn in train_dataset_task_files}\n",
    "        datasets.update(train_dfs_)\n",
    "    else:\n",
    "        train_df_fn = f'ds_{dataset_num}__task_{task_num}_train_set_{sample_size}'\n",
    "        datasets[train_df_fn] = pd.read_csv(os.path.join(data_dir, train_df_fn + '.csv'), encoding='utf-8')\n",
    "\n",
    "        if train_df_fn not in [os.path.basename(fn).strip('.csv') for fn in train_dataset_task_files]:\n",
    "            raise ValueError(f\"Sample size {sample_size} not found for\"\n",
    "                             f\" dataset {dataset_num} and task {task_num}\")\n",
    "\n",
    "    return datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "904e16c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_training_example(system_prompt, user_prompt_format, user_prompt_text, completion):\n",
    "    return {'messages': [\n",
    "        {'role': 'system',\n",
    "         'content': system_prompt},\n",
    "\n",
    "        {'role': 'user',\n",
    "         'content': user_prompt_format.format(text=user_prompt_text)},\n",
    "\n",
    "        {'role': 'assistant',\n",
    "         'content': completion}\n",
    "    ]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdf692a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets):\n",
    "    hatespeech_open_ai_metadata = list()\n",
    "\n",
    "    df_id_metadata = pd.DataFrame() if not os.path.exists('hatespeech_open_ai_metadata.csv') \\\n",
    "        else pd.read_csv('hatespeech_open_ai_metadata.csv')\n",
    "\n",
    "    for df_name, df in datasets.items():\n",
    "        df_jsonl_filename = os.path.join(output_dir, 'temp', df_name + '.jsonl')\n",
    "        write_jsonl(data_list=df['openai_instance_format'].tolist(), filename=df_jsonl_filename)\n",
    "\n",
    "        if not_use_full_labels:\n",
    "            df_name += '_single_letter_labels'\n",
    "\n",
    "        if (not rewrite_df_in_openai and\n",
    "                (len(df_id_metadata) > 0 and df_name in df_id_metadata['df_name'].tolist())):\n",
    "            print(f\"Dataset {df_name} already uploaded to OpenAI\")\n",
    "            continue\n",
    "\n",
    "        print(f\"Uploading {df_name} to OpenAI\")\n",
    "        df_response = client.files.create(\n",
    "            file=open(df_jsonl_filename, \"rb\"), purpose=\"fine-tune\"\n",
    "        )\n",
    "        df_response_dict = df_response.to_dict()\n",
    "        df_file_id = df_response_dict[\"id\"]\n",
    "\n",
    "        # Wait until the file is processed\n",
    "        while True:\n",
    "            file = client.files.retrieve(df_file_id)\n",
    "            file_dict = file.to_dict()\n",
    "            if file_dict[\"status\"] == \"processed\":\n",
    "                break\n",
    "            time.sleep(15)\n",
    "        hatespeech_open_ai_metadata.append({'df_name': df_name, 'file_id': df_file_id})\n",
    "\n",
    "    df_id_metadata = pd.concat([df_id_metadata, pd.DataFrame(hatespeech_open_ai_metadata)])\n",
    "    df_id_metadata.to_csv('hatespeech_open_ai_metadata.csv', index=False)\n",
    "\n",
    "    return df_id_metadata\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ace921b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name, n_epochs):\n",
    "    response = client.fine_tuning.jobs.create(\n",
    "        training_file=training_file_id,\n",
    "        validation_file=evaluation_file_id,\n",
    "        model=\"gpt-4o-mini-2024-07-18\",\n",
    "        suffix=model_name,\n",
    "        hyperparameters={\"n_epochs\": n_epochs,\n",
    "                         \"batch_size\": n_batch}\n",
    "    )\n",
    "\n",
    "    response_dict = response.to_dict()\n",
    "    job_id = response_dict[\"id\"]\n",
    "    print(\"Job ID:\", response_dict[\"id\"])\n",
    "    print(\"Status:\", response_dict[\"status\"])\n",
    "\n",
    "    # Wait until the job is done\n",
    "    while True:\n",
    "        job = client.fine_tuning.jobs.retrieve(job_id)\n",
    "        job_dict = job.to_dict()\n",
    "        if job_dict[\"status\"] == \"succeeded\":\n",
    "            break\n",
    "        elif job_dict[\"status\"] == \"failed\":\n",
    "            raise Exception(\"Training failed: %s\" % job_dict[\"error\"])\n",
    "        time.sleep(30)\n",
    "\n",
    "    return job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2026f811",
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_and_log_finetuning_event_history(job_id):\n",
    "    response = client.fine_tuning.jobs.list_events(fine_tuning_job_id=job_id)\n",
    "    response_dict = response.to_dict()\n",
    "    events = response_dict[\"data\"]\n",
    "    events.reverse()\n",
    "    for event in events:\n",
    "        print(event[\"message\"])\n",
    "\n",
    "    # Log events\n",
    "    for event in events:\n",
    "        if event['type'] != 'metrics':\n",
    "            continue\n",
    "        data = event['data']\n",
    "        wandb.log(\n",
    "            {\n",
    "                \"train_loss\": data.get(\"train_loss\"),\n",
    "                \"valid_loss\": data.get(\"valid_loss\"),\n",
    "                \"train_mean_token_accuracy\": data.get(\"train_mean_token_accuracy\"),\n",
    "                \"valid_mean_token_accuracy\": data.get(\"valid_mean_token_accuracy\")\n",
    "            },\n",
    "            step=data.get('step', 0)\n",
    "        )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebc50a97",
   "metadata": {},
   "source": [
    "# Main Implementation\n",
    "This section contains the main code for fine-tuning the ChatGPT model. It includes setup, data preparation, training, and evaluation steps.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1b7246",
   "metadata": {},
   "source": [
    "### Connect to WandB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "37564c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Failed to detect the name of this notebook, you can set it manually with the WANDB_NOTEBOOK_NAME environment variable to enable code saving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkublima\u001b[0m (\u001b[33mdigdemlab\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "wandb version 0.19.7 is available!  To upgrade, please run:\n",
       " $ pip install wandb --upgrade"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Tracking run with wandb version 0.15.11"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Run data is saved locally in <code>e:\\Dropbox\\projects_active\\eth_uzh_hatespeech_llm_finetuning\\src\\gpt_finetuning\\wandb\\run-20250228_140735-lw1j9j4o</code>"
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Syncing run <strong><a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o' target=\"_blank\">finetune_chetGPT_hatespeech_prolific</a></strong> to <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View project at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run at <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x1814eba77c0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 1/2\n",
    "# Initialize the Weights and Biases run\n",
    "wandb.init(\n",
    "    # set the wandb project where this run will be logged\n",
    "    project=WANDB_PROJECT_NAME,\n",
    "    name=run_name if run_name != '' else f'{MODEL_NAME}_ds_{dataset}_task_{int(task)}'\n",
    "                                                    f'_sample_{sample_size}_epochs_{n_epochs}'\n",
    "                                                    f'_full_label_names_{str(not not_use_full_labels)}'\n",
    "                                                    f'_temp_{temp}',\n",
    "\n",
    "    # track hyperparameters and run metadata\n",
    "    config = {\n",
    "        \"model\": MODEL_NAME,\n",
    "        \"dataset\": dataset_num_to_dataset_name[int(dataset)],\n",
    "        \"task\": task_num_to_task_name[int(task)],\n",
    "        \"epochs\": n_epochs,\n",
    "        \"temp\": temp\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58f1df25",
   "metadata": {},
   "source": [
    "### Load and Process Data Sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2454b3ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 3\n",
    "# Load the dataset and filename\n",
    "dataset_idx, dataset_task_mappings = load_dataset_task_prompt_mappings(\n",
    "    dataset_num=dataset, task_num=task, dataset_task_mappings_fp=dataset_task_mappings_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d35437fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 5\n",
    "# Load the train and eval datasets\n",
    "datasets = load_train_and_eval_sets(\n",
    "    data_dir=data_dir, dataset_num=dataset, task_num=task, sample_size=sample_size, dataset_eval=dataset_eval)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f379906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact prompts>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 4\n",
    "# Get information specific to the dataset\n",
    "label_column = dataset_task_mappings.loc[dataset_idx, \"label_column\"]\n",
    "system_prompt = dataset_task_mappings.loc[dataset_idx, 'zero_shot_prompt']\n",
    "user_prompt_format = dataset_task_mappings.loc[dataset_idx, 'user_prompt']\n",
    "    \n",
    "# Log the system prompt and user_prompt_format as files in wandb\n",
    "prompts_artifact = wandb.Artifact('prompts', type='prompts')\n",
    "with prompts_artifact.new_file('system_prompt.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(system_prompt)\n",
    "with prompts_artifact.new_file('user_prompt_format.txt', mode='w', encoding='utf-8') as f:\n",
    "    f.write(user_prompt_format)\n",
    "wandb.run.log_artifact(prompts_artifact)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "47917349",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 6\n",
    "# Generate the training and evaluation examples in the way expected by the Open AI API to finetune chatgpt3.5\n",
    "preprocessed_output_dir = os.path.join(\n",
    "    output_dir, 'preprocessed', 'full_name_labels' if not not_use_full_labels else 'single_letter_labels')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5a9616d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 7\n",
    "for df_name, df in datasets.items():\n",
    "    df['completion_label'] = df[label_column].map(\n",
    "        lambda label: map_label_to_completion(label=label, task_num = task,\n",
    "                                              full_label=not not_use_full_labels)\n",
    "        )\n",
    "    df['openai_instance_format'] = df.apply(\n",
    "        lambda row: create_training_example(\n",
    "            system_prompt=system_prompt, user_prompt_format=user_prompt_format,\n",
    "            user_prompt_text=row['text'],\n",
    "            completion=row['completion_label']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    df['openai_instance_without_completion'] = df['openai_instance_format'].map(lambda x: x['messages'][:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8bcd8c12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Check for errors ds_5__task_1_train_set_100 set: \n",
      "No errors found\n"
     ]
    }
   ],
   "source": [
    "# Section 8\n",
    "print(f'Check for errors {df_name} set: ')\n",
    "assert not dataset_has_format_errors(df['openai_instance_format'].tolist()), f\"Errors found in {df_name}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9b1d7833",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 9\n",
    "df.to_csv(os.path.join(preprocessed_output_dir, df_name + '.csv'), index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24cdc6b4",
   "metadata": {},
   "source": [
    "### Upload Training Dataset to OPEN AI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "2fd9b187",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uploading ds_5__task_1_eval_set to OpenAI\n",
      "Uploading ds_x__task_1_full_eval to OpenAI\n",
      "Uploading ds_5__task_1_train_set_100 to OpenAI\n"
     ]
    }
   ],
   "source": [
    "# Section 10\n",
    "# Create jsonl file and upload to OpenAI\n",
    "df_id_metadata =upload_datasets_to_openai(output_dir, not_use_full_labels, rewrite_df_in_openai, datasets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "705758da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 11\n",
    "# delete all files in the temp folder\n",
    "os.system(f\"rm -rf {os.path.join(output_dir, 'temp')}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4519ab5a",
   "metadata": {},
   "source": [
    "### Finetune chatGPT with the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b0ec6e82",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 12\n",
    "# Run training on the train_df samples selected\n",
    "eval_set_name = f'ds_{dataset}__task_{task}_eval_set'\n",
    "eval_df = datasets[eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a76b1ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finetuning ds_5__task_1_train_set_100\n",
      "--------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "# Section 13\n",
    "print(f\"Finetuning {df_name}\")\n",
    "print('-' * 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a1036bc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 14\n",
    "# Log in wandb.config the dataset sample size used\n",
    "sample_size = df_name.split('_')[-1]\n",
    "wandb.config['trainset_size'] = sample_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d0860e65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the evaluation set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 847, 999\n",
      "mean / median: 879.7842639593908, 869.0\n",
      "p5 / p95: 853.0, 920.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 5.187817258883249, 6.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~346635 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~1733175 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 15\n",
    "# Check statistics and possible cost for each dataset\n",
    "print('Statistics for the evaluation set: ')\n",
    "eval_tokens = check_token_statistics_and_cost_estimate(\n",
    "    datasets[eval_set_name]['openai_instance_format'].tolist(), target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "254bdfe1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics for the training set: \n",
      "\n",
      "#### Distribution of num_messages_per_example:\n",
      "min / max: 3, 3\n",
      "mean / median: 3.0, 3.0\n",
      "p5 / p95: 3.0, 3.0\n",
      "\n",
      "#### Distribution of num_total_tokens_per_example:\n",
      "min / max: 848, 997\n",
      "mean / median: 879.12, 867.5\n",
      "p5 / p95: 853.0, 918.0\n",
      "\n",
      "#### Distribution of num_assistant_tokens_per_example:\n",
      "min / max: 4, 6\n",
      "mean / median: 4.88, 4.0\n",
      "p5 / p95: 4.0, 6.0\n",
      "\n",
      "0 examples may be over the 4096 token limit, they will be truncated during fine-tuning\n",
      "Dataset has ~87912 tokens that will be charged for during training\n",
      "By default, you'll train for 5 epochs on this dataset\n",
      "By default, you'll be charged for ~439560 tokens\n"
     ]
    }
   ],
   "source": [
    "# Section 16\n",
    "print(f'Statistics for the training set: ')\n",
    "train_tokens = check_token_statistics_and_cost_estimate(df['openai_instance_format'].tolist(),\n",
    "                                                        target_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "91aed7d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total number of tokens: 434547\n",
      "\n",
      "#### Estimated cost: 3.48 USD\n"
     ]
    }
   ],
   "source": [
    "# Section 17\n",
    "print(f\"Total number of tokens: {eval_tokens + train_tokens}\\n\")\n",
    "print(f\"#### Estimated cost: {round((eval_tokens + train_tokens) * (0.008 / 1000), 2)} USD\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5a4cb776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 18\n",
    "# Run the fine-tuning job\n",
    "training_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == df_name, 'file_id'].values[0]\n",
    "evaluation_file_id = df_id_metadata.loc[df_id_metadata['df_name'] == eval_set_name, 'file_id'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6b0baf56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job ID: ftjob-GbT4yymcYTkiVKICP96Cwfnb\n",
      "Status: validating_files\n"
     ]
    }
   ],
   "source": [
    "# Section 19\n",
    "model_name = (df_name.replace('__', '_')\n",
    "              .replace('train_set', 'trn')\n",
    "              .replace('task', 't')\n",
    "              .replace('_single_letter_labels', '_sl'))\n",
    "job_id = fine_tune_chat_gpt(evaluation_file_id, training_file_id, model_name=model_name, n_epochs=n_epochs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6aff8caf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The model ft:gpt-4o-mini-2024-07-18:university-of-zurich:ds-5-t-1-trn-100:B5uXvIeg has been successfully fine-tuned\n"
     ]
    }
   ],
   "source": [
    "# Section 20\n",
    "# Print the model name\n",
    "response = client.fine_tuning.jobs.retrieve(job_id)\n",
    "response_dict = response.to_dict()\n",
    "full_model_name = response_dict[\"fine_tuned_model\"]\n",
    "print(f'The model {full_model_name} has been successfully fine-tuned')\n",
    "wandb.config['model_name_openai'] = full_model_name\n",
    "wandb.config['finetuning_jobid'] = job_id\n",
    "wandb.config['training_file_openai_id'] = response_dict['training_file']\n",
    "wandb.config['validation_file_openai_id'] = response_dict['validation_file']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45b169bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step 10/25: training loss=0.08, validation loss=0.09, full validation loss=0.10\n",
      "Step 11/25: training loss=0.03, validation loss=0.09\n",
      "Step 12/25: training loss=0.07, validation loss=0.03\n",
      "Step 13/25: training loss=0.04, validation loss=0.24\n",
      "Step 14/25: training loss=0.04, validation loss=0.26\n",
      "Step 15/25: training loss=0.05, validation loss=0.19, full validation loss=0.15\n",
      "Step 16/25: training loss=0.03, validation loss=0.11\n",
      "Step 17/25: training loss=0.03, validation loss=0.10\n",
      "Step 18/25: training loss=0.03, validation loss=0.06\n",
      "Step 19/25: training loss=0.04, validation loss=0.21\n",
      "Step 20/25: training loss=0.01, validation loss=0.11, full validation loss=0.12\n",
      "Step 21/25: training loss=0.01, validation loss=0.07\n",
      "Step 22/25: training loss=0.01, validation loss=0.09\n",
      "Step 23/25: training loss=0.00, validation loss=0.23\n",
      "Step 24/25: training loss=0.01, validation loss=0.26\n",
      "Step 25/25: training loss=0.00, validation loss=0.17, full validation loss=0.14\n",
      "Checkpoint created at step 15\n",
      "Checkpoint created at step 20\n",
      "New fine-tuned model created\n",
      "The job has successfully completed\n"
     ]
    }
   ],
   "source": [
    "# Section 21\n",
    "# Print the events (training history of the model)\n",
    "print_and_log_finetuning_event_history(job_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c579b37",
   "metadata": {},
   "source": [
    "## Evaluate Model on Validation Set with Annotations of the Group from which the Training Data is!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fba537b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Section 22\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0a544a5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 394/394 [04:02<00:00,  1.62it/s]\n"
     ]
    }
   ],
   "source": [
    "# Section 23\n",
    "for messages in tqdm(eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "337b13ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 24\n",
    "# Add predictions to df\n",
    "eval_df['prediction'] = predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fe242636",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 25\n",
    "# Store output\n",
    "predictions_output_dir = os.path.join(output_dir, 'predictions',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir, exist_ok=True)\n",
    "datasets[eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bcaddde5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 26\n",
    "# Get performance metrics\n",
    "y_true = eval_df['completion_label']\n",
    "y_pred = eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "3d085eda",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 27\n",
    "label_type = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels = task_to_display_labels[task][label_type]\n",
    "labels = display_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "a1ce1492",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.707395498392283,\n",
      "                 'precision': 0.7189542483660131,\n",
      "                 'recall': 0.6962025316455697,\n",
      "                 'support': 158},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.8033826638477801,\n",
      "                       'precision': 0.7883817427385892,\n",
      "                       'recall': 0.8189655172413793,\n",
      "                       'support': 232},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.0,\n",
      "                  'precision': 0.0,\n",
      "                  'recall': 0.0,\n",
      "                  'support': 4},\n",
      " 'accuracy': 0.7614213197969543,\n",
      " 'macro avg': {'f1-score': 0.5035927207466877,\n",
      "               'precision': 0.5024453303682007,\n",
      "               'recall': 0.5050560162956497,\n",
      "               'support': 394},\n",
      " 'weighted avg': {'f1-score': 0.7567341795905221,\n",
      "                  'precision': 0.7525363846629004,\n",
      "                  'recall': 0.7614213197969543,\n",
      "                  'support': 394}}\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Section 28\n",
    "cm_plot, classification_report, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true, y_pred, display_labels, labels, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "75c64dc1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 29\n",
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "4aaa4209",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Section 31\n",
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "45aaca05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_ds_5_t_1_trn_100>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Section 32\n",
    "# Log the classification report as an artifact\n",
    "classification_report = (pd.DataFrame({k: v for k, v in classification_report.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report': wandb.Table(\n",
    "    dataframe=classification_report)})\n",
    "\n",
    "classification_report_artifact = wandb.Artifact(\n",
    "      f'classification_report_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact.new_file('classification_report.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6db7bbf2",
   "metadata": {},
   "source": [
    "## Evaluation on Second Evaluation Set\n",
    "In this section, we will evaluate the performance of our model on a second evaluation set (`second_eval_set`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8cbb7ff7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['ds_5__task_1_eval_set', 'ds_x__task_1_full_eval', 'ds_5__task_1_train_set_100'])\n"
     ]
    }
   ],
   "source": [
    "print(datasets.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_eval_set_name = f'ds_{dataset_eval}__task_{task}_full_eval'\n",
    "full_eval_df = datasets[second_eval_set_name]\n",
    "for df_name, df in datasets.items():\n",
    "    if df_name == second_eval_set_name:\n",
    "        continue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9cfc903c",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_eval_df = datasets[second_eval_set_name]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "303aeed5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "##################################################\n",
      "Getting predictions on the evaluation set\n"
     ]
    }
   ],
   "source": [
    "# Load the second evaluation set (second_eval_set)\n",
    "# Evaluate the model on the evaluation set and store the predictions\n",
    "print(\"\\n\" + \"#\" * 50)\n",
    "print(\"Getting predictions on the evaluation set\")\n",
    "predictions_2 = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7c218926",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 494/494 [04:38<00:00,  1.78it/s]\n"
     ]
    }
   ],
   "source": [
    "for messages in tqdm(full_eval_df['openai_instance_without_completion'].tolist()):\n",
    "    # Retry the completion at least COMPLETION_RETRIES times\n",
    "    num_retries = 2\n",
    "    response = None\n",
    "    while num_retries < COMPLETION_RETRIES and response is None:\n",
    "        try:\n",
    "            response = client.chat.completions.create(\n",
    "                model=full_model_name,\n",
    "                messages=messages,\n",
    "                temperature=temp,\n",
    "                n=1\n",
    "            )\n",
    "        except Exception as e:\n",
    "            print('Error getting predictions. Retrying...')\n",
    "            time.sleep(5)\n",
    "            num_retries += 1\n",
    "            if num_retries >= COMPLETION_RETRIES:\n",
    "                print('Maximum amount of retires reached')\n",
    "                raise e\n",
    "    response_dict = response.to_dict()\n",
    "    predictions_2.append(response_dict['choices'][0]['message']['content'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "789fce14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add predictions to df\n",
    "full_eval_df['prediction'] = predictions_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "616c4658",
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions_output_dir_2 = os.path.join(output_dir, 'predictions_2',\n",
    "                                      f'dataset_{dataset}_task_{task}')\n",
    "os.makedirs(predictions_output_dir_2, exist_ok=True)\n",
    "datasets[second_eval_set_name].to_csv(\n",
    "    os.path.join(predictions_output_dir_2, f\"{model_name}-{run_name}.csv\"),\n",
    "    index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ec8481ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get performance metrics\n",
    "y_true_2 = full_eval_df['completion_label']\n",
    "y_pred_2 = full_eval_df['prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "296deb14",
   "metadata": {},
   "outputs": [],
   "source": [
    "label_type_2 = 'full_name' if not not_use_full_labels else 'short_name'\n",
    "display_labels_2 = task_to_display_labels[task][label_type]\n",
    "labels_2 = display_labels_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "83dbd847",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "c:\\Users\\maelk\\miniconda3\\envs\\chatGPT\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'HATE SPEECH': {'f1-score': 0.5982905982905983,\n",
      "                 'precision': 0.5072463768115942,\n",
      "                 'recall': 0.7291666666666666,\n",
      "                 'support': 144},\n",
      " 'KEINE HATE SPEECH': {'f1-score': 0.7992633517495396,\n",
      "                       'precision': 0.7560975609756098,\n",
      "                       'recall': 0.84765625,\n",
      "                       'support': 256},\n",
      " 'TOXIC SPEECH': {'f1-score': 0.0,\n",
      "                  'precision': 0.0,\n",
      "                  'recall': 0.0,\n",
      "                  'support': 94},\n",
      " 'accuracy': 0.6518218623481782,\n",
      " 'macro avg': {'f1-score': 0.465851316680046,\n",
      "               'precision': 0.421114645929068,\n",
      "               'recall': 0.5256076388888888,\n",
      "               'support': 494},\n",
      " 'weighted avg': {'f1-score': 0.5885936522302192,\n",
      "                  'precision': 0.5396851292927645,\n",
      "                  'recall': 0.6518218623481782,\n",
      "                  'support': 494}}\n"
     ]
    },
    {
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      "text/plain": [
       "<Figure size 1500x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cm_plot, classification_report_2, metrics = plot_count_and_normalized_confusion_matrix(\n",
    "    y_true_2, y_pred_2, display_labels_2, labels_2, xticks_rotation='horizontal')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "20289ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log metrics\n",
    "for metric_name, metric_value in metrics.items():\n",
    "    wandb.log({metric_name: metric_value})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "09a5abfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Log the confusion matrix matplotlib figure\n",
    "wandb.log({'confusion_matrix_2': wandb.Image(cm_plot)})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2399e29a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Artifact classification_report_2_ds_5_t_1_trn_100>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log the classification report as an artifact\n",
    "classification_report_2 = (pd.DataFrame({k: v for k, v in classification_report_2.items() if k != 'accuracy'})\n",
    "                         .transpose().reset_index())\n",
    "\n",
    "wandb.log({'classification_report_2': wandb.Table(\n",
    "    dataframe=classification_report_2)})\n",
    "\n",
    "classification_report_artifact_2 = wandb.Artifact(\n",
    "      f'classification_report_2_{model_name}', type='classification_report')\n",
    "\n",
    "with classification_report_artifact_2.new_file('classification_report_2.txt', mode='w') as f:\n",
    "    f.write(pprint.pformat(classification_report_2))\n",
    "\n",
    "wandb.run.log_artifact(classification_report_artifact_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b0aa2d8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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      "text/html": [
       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>█▁</td></tr><tr><td>f1</td><td>█▁</td></tr><tr><td>precision</td><td>█▁</td></tr><tr><td>recall</td><td>█▁</td></tr><tr><td>train_loss</td><td>█▃▇▅▄▆▃▃▃▄▁▂▁▁▁▁</td></tr><tr><td>train_mean_token_accuracy</td><td>▁▆▂▅▆▄▇▆▇▆█▇████</td></tr><tr><td>valid_loss</td><td>▃▃▁▇█▆▃▃▂▆▃▂▃▇█▅</td></tr><tr><td>valid_mean_token_accuracy</td><td>▂▁█▃▄▂▅▅▇▂▄▆▅▂▅▅</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>accuracy</td><td>0.65182</td></tr><tr><td>f1</td><td>0.65182</td></tr><tr><td>precision</td><td>0.65182</td></tr><tr><td>recall</td><td>0.65182</td></tr><tr><td>train_loss</td><td>0.00137</td></tr><tr><td>train_mean_token_accuracy</td><td>1.0</td></tr><tr><td>valid_loss</td><td>0.16761</td></tr><tr><td>valid_mean_token_accuracy</td><td>0.96479</td></tr></table><br/></div></div>"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">finetune_chetGPT_hatespeech_prolific</strong> at: <a href='https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o' target=\"_blank\">https://wandb.ai/digdemlab/chatgpt_annotations_llm_comparison/runs/lw1j9j4o</a><br/>Synced 6 W&B file(s), 4 media file(s), 4 artifact file(s) and 0 other file(s)"
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20250228_140735-lw1j9j4o\\logs</code>"
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      "text/plain": [
       "<IPython.core.display.HTML object>"
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   ],
   "source": [
    "# Mark the end of the run\n",
    "wandb.finish()"
   ]
  }
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  "kernelspec": {
   "display_name": "chatGPT",
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